Method for determining the geometry of a defect based on non-destructive measurement methods using direct inversion

Abstract

Method for determining the geometry of one or more real, examined defects of a metallic, in particular magnetizable object, in particular a pipe or a tank, by means of at least two reference data sets of the object generated on the basis of different, non-destructive measurement methods,wherein the object is at least partially represented on or by an at least two-dimensional, preferably three-dimensional, object grid, in an EDP unit,wherein an output defect geometry, in particular on the object grid or an at least two-dimensional defect grid, is generated by inversion of at least parts of the reference data sets, in particular by at least one neural network (NN) trained for this object, a respective prediction data set for the non-destructive measurement methods used in the generation of the reference data sets is calculated on the basis of the output defect geometry by a simulation routine, a comparison of at least parts of the prediction data sets with at least parts of the reference data sets is carried out and, depending on at least one accuracy measure, the method for determining the geometry of the defect is terminated or an iterative adjustment of the output defect geometry to the geometry of the real defect(s) is carried out, as well as methods for determining a load limit (FIG. 1).

1 . Method for determining the geometry of one or more real, examined defects of a metallic, in particular magnetizable object, in particular a pipe or a tank, by means of at least two reference data sets of the object generated on the basis of different, non-destructive measurement methods,
 wherein the object is at least partially represented on or by an at least two-dimensional, preferably three-dimensional, object grid, in an EDP unit,   wherein an output defect geometry, in particular on the object grid or an at least twodimensional defect grid, is generated by inversion of at least parts of the reference data sets, in particular by at least one neural network (NN) trained for this object, a respective prediction data set for the non-destructive measurement methods used in the generation of the reference data sets is calculated on the basis of the output defect geometry by a simulation routine, a comparison of at least parts of the prediction data sets with at least parts of the reference data sets is carried out and, depending on at least one accuracy measure, the method for determining the geometry of the defect is terminated or an iterative adjustment of the output defect geometry to the geometry of the real defect(s) is carried out.   
     
     
         2 . The method according to  claim 1 , characterized in that a training simulation routine generates training data by simulation based on different training geometries, with which a neural network (NN) is trained to invert the measurement data. 
     
     
         3 . The method according to any one of  claim 1  or  2 , characterized in that the neural network (NN) is trained based on data from a database containing simulated measurements. 
     
     
         4 . The method according to any one of  claims 1  to  3 , characterized in that input data for the neural network (NN) are extracted from a reference data set by a feature extractor (FE), which is preferably designed as a further neural network. 
     
     
         5 . The method according to any one of  claims 1  to  4 , characterized in that by means of the neural network (NN) input data with a two-dimensional spatial resolution are converted into an output defect geometry with a three-dimensional spatial resolution. 
     
     
         6 . The method according to any one of  claims 1  to  5 , characterized in that a classification of defects is performed by the neural network (NN). 
     
     
         7 . The method according to any one of  claims 1  to  6 , characterized in that a data set based on an MFL, eddy current, EMAT or ultrasound measurement method is used as a first reference data set and at least one further reference data set is a data set generated on the basis of a further measurement method generating from this group of measurement methods. 
     
     
         8 . The method according to any one of  claims 1  to  7 , characterized in that the iterative adjustment of the output defect geometry to the geometry of the real defect(s) is carried out by means of the EDP unit and by means of at least one, preferably several, expert routines ( 11 ) running in particular in competition and further in particular in parallel with each other,
 wherein in the respective expert routine(s) ( 11 ) a respective expert defect geometry is generated by means of at least one own algorithm and on the basis of the output defect geometry, 
 on the basis of the respective expert defect geometry, respective expert prediction data sets are determined by simulation or assignment of a measurement corresponding to the respective reference data set, 
 and the expert defect geometry on which the respective expert prediction data sets are based is then made available to at least one, in particular all, of the expert routines ( 11 ) as a new output defect geometry for further adjustment to the geometry of the real defect(s), if the expert prediction data sets of a respective expert routine are more similar to the respective reference data sets than the output prediction data sets and/or a fitness function considering the at least two expert prediction data sets is improved, 
 and then the expert prediction data sets associated with the new output defect geometry are used as the new output prediction data sets, 
 wherein the iterative adjustment is performed by means of the expert routines ( 11 ) until a stop criterion is satisfied. 
 
     
     
         9 . The method according to  claim 8 , characterized in that the expert routines ( 11 ) run in competition with one another in such a way that a distribution of the resources of the EDP unit to a respective expert routine, in particular in the form of computing time, preferably CPU time and/or GPU time, as a function of a success rate, in which in particular the number of output defect location geometries calculated by this expert routine and made available for one or more other expert routines ( 11 ) is taken into account, and/or as a function of a reduction of the fitness function, in which in particular the number of expert prediction data sets generated for the reduction is taken into account. 
     
     
         10 . The method according to any one of the preceding claims, characterized in that, in order to determine the object grid, a classification of anomaly-free areas and anomalyaffected areas of the object is first carried out on the basis of at least parts of the reference data sets, wherein an output object grid is produced in particular on the basis of previously known information about the object, prediction data sets for the respective non-destructive measurement methods are calculated using the output object grid, at least parts of the prediction data sets are compared with respective parts of the reference data sets with exclusion of the anomaly-affected areas, and the output object grid is used as an object grid describing the geometry of the object as a function of at least one accuracy measure, or the output object grid is iteratively adjusted to the geometry of the object in the anomalyfree areas by means of the EDP unit. 
     
     
         11 . The method according to  claim 10 , characterized in that, in the iterative adjustment of the output object grid, a new output object grid is created and new prediction data sets are calculated for it, and a comparison of at least parts of the new prediction data sets with corresponding parts of the reference data sets is carried out with exclusion of the anomaly-affected areas until an object stop criterion is satisfied, wherein the output object grid then present is used as an object grid describing the geometry of the object. 
     
     
         12 . The method according to any one of the preceding  claim 10  or  11 , characterized in that during the classification an assignment of an anomaly-free area to at least one predefined local element of the object is performed and this is used in the creation of the output object grid or is inserted into the output object grid. 
     
     
         13 . The method according to  claim 12 , characterized in that the local element, which is formed in particular in the form of a weld seam, is described by means of a parametric geometry model. 
     
     
         14 . The method according to any one of  claims 8  to  13 , characterized in that a comparison of the variation of the expert prediction data set with the measurement variation of the real data set is used as stop criterion. 
     
     
         15 . The method according to any one of  claims 8  to  14 , characterized in that different and defect-specific variations are made in the expert routine or routines ( 11 ) for generating the expert defect geometry, wherein in particular a first expert routine ( 11 ) is provided for varying cracks, another for varying corrosion and/or another for varying lamination defects. 
     
     
         16 . A method for determining a load limit of an object which is pressurized at least during operation and is designed in particular as an oil, gas or water pipeline, in which a data set describing one or more defect(s) is used as an input data set in a calculation of the load limit, characterized in that the input data set is first determined in accordance with a method according to any one of the preceding claims.